Damage identification of a beam with a variable cross-sectional area

In the paper, the process of identification of crack parameters occurring in the cantilever beam with the variable cross-sectional area has been presented. For identification, the non-destructive vibration method has been applied. The analytical solution of the free vibration problem of the beam described according to the Bernoulli-Euler theory has been obtained with the help of Green’s functions

STR-906: COMPUTER-IMAGE-BASED LOOSENED BOLT DETECTION USING SUPPORT VECTOR MACHINES

Despite many contact-sensor-based methods have been proposed to monitor and detect structural defects, there are still difficulties compensating for environmental effects and malfunctions of attached sensors, which are main reasons for transmitting false signals. Moreover, regardless of releasing correct or incorrect signals, it eventually leads to human-conducted on-site inspections. In light of these shortcomings, vision-based inspections are considered as potential approach to overcome the explained issues. A number of vision-based methods for detecting damages from images have been developed. However, there are only a few vision-based methods for detecting loosened bolts. Thus, a computer-vision method for detecting loosened bolts is proposed. This study includes two algorithms. The first one is a preprocessing to crop bolt images from bolted-joint images. The second algorithm is a feature extraction by integrating previously proposed algorithms in computer-vision. To accomplish an automated inspection, linear support vector machine is trained and used as a classifier. The robustness of the proposed is verified by the experimental validation; 22 bolt images are used to build a classifier, and 40 bolt images are tested

Vision-based Crack Identification on the Concrete Slab Surface using Fuzzy Reasoning Rules and Self-Organizing

Identifying cracks on the surface of concrete slab structure is important for structure stability maintenance. In order to avoid subjective visual inspection, it is necessary to develop an automated identification and measuring system by vision based method. Although there have been some intelligent computerized inspection methods, they are sensitive to noise due to the brightness contrast and objects such as forms and joints of certain size often falsely classified as cracks. In this paper, we propose a new fuzzy logic based image processing method that extracts cracks from concrete slab structure including small cracks that were often neglected as noise. We extract candidate crack areas by applying fuzzy method with three color channel values of concrete slab structure. Then further refinement processes are performed with Self Organizing Map algorithm and density based noise removal process to obtain basic crack characteristic attributes for further analysis. Experimental result verifies that the proposed method is sufficiently identified cracks with various sizes with high accuracy (97.3%) among 1319 ground truth cracks from 30 images

Basic Study on Detection of Deteriorated RC Structures Using Infrared Thermography Camera

Infrastructures in the world get old in several decades of their service time. Falling-offs of parts of deteriorated structures were often reported and sometimes caused casualties in Japan and many other countries. When an earthquake occurs, in particular, deteriorated structures have higher possibility to be damaged or collapsed. Thus assessing the health condition of structures is one of the important topics in civil engineering. Considering a large number of structures that have been in service more than 40 years in Japan, efficient evaluation methods are requested. In this regard, non-destructive tests have high possibility to be applied to various structures without affecting their functions. Accordingly, this study focuses on the use of infrared thermography to detect internal deterioration of concrete structures. As a first step of investigation, thermography diagnosis, hammer sounding test and Schmidt rebound hammer test were carried out to detect internal deterioration of a concrete retaining wall located in the campus of Chiba University, Chiba, Japan, and the results were compared to evaluate the capability and accuracy of these diagnosis methods

Erkennung von Rissen mittels maschinellen Lernens

Instandhaltung ist eines der zentralen Themen für den Betreiber eines Schienennetzes. Um einen planmäßigen Schienenverkehr sicherzustellen, muss die zugrunde liegende Infrastruktur betriebsbereit gehalten werden. Dies geschieht, indem der momentane technische bzw. betriebliche Zustand der Infrastruktur bestimmt wird und Schäden, wie z. B. Risse in Schwellen, rechtzeitig erkannt werden. Vor diesem Hintergrund wird in diesem Beitrag dargestellt, wie ein modernes Verfahren des maschinellen Lernens für die Erkennung von Rissen in Bahnschwellen in das bestehende Erfassungssystem eines Messzuges der DB Netz AG (DB Netz) integriert worden ist

Semantic Segmentation Using Modified U-Net Architecture for Crack Detection

The visual inspection of a concrete crack is essential to maintaining its good condition during the service life of the bridge. The visual inspection has been done manually by inspectors, but unfortunately, the results are subjective. On the other hand, automated visual inspection approaches are faster and less subjective. Concrete crack is an important deficiency type that is assessed by inspectors. Recently, various Convolutional Neural Networks (CNNs) have become a prominent strategy to spot concrete cracks mechanically. The CNNs outperforms the traditional image processing approaches in accuracy for the high-level recognition task. Of them, U-Net, a CNN based semantic segmentation method, has been one of the most popular in the deep learning because of its excellent performance in open-source crack classification. Although the results of the trained U-Net look good for some dataset, the model still requires further improvement for the set of hard examples of concrete crack that contains the stain, waterspot, and small width crack. In this paper, we address the challenging problem of accurately detecting a thin concrete crack. We designed a U-Net like structure that has a contracting path and an expansive path to overcome this challenge and compared it to current models, including original U-Net and pyramid pooling module network. The proposed architecture utilizes multiple feature maps in a down-sampling path to obtain a higher pixel-level segmentation precision. The down-sampled feature is then up-sampled from the output of the pyramid pooling module [13], giving a binary crack and non-crack semantic segmentation. In the experiment, we have collected hard examples and evaluated the approach. Experimental results demonstrate that the proposed network outperforms the U-Net and a pyramid pooling module network in detecting a thin crack in a noisy environment

Characterization of Cracks in Historical Buildings Using Image-Processing Techniques

Cracks are structural pathologies that affect the structural integrity of historical buildings. The methodologies commonly used to detect cracks are based on visual inspections or in intrusive techniques that involve removing external wall layers. The main objective of this study is to develop and validate a semi-automatic and non-destructive tool that helps the user to analyze the position and growth of the cracks in masonry constructions based on a photogrammetry analysis. The developed tool uses image processing to plot a curve of the crack area, and, in case needed, its evolution over time. The tool was validated in laboratory using earthen samples that were subjected to uniaxial compression tests. The research also provides the results of the tool used in a case study of a 16th Century stone masonry church located in the main square of Cusco; Southern Peru. This case study validates the qualitative metrics of the present work, and indicates that the tool provided accurate results when compared to the ground truth, which could be helpful in future research studies in order to automatize crack monitoring

Automated Damage Index Estimation of Reinforced Concrete Columns for Post-Earthquake Evaluations

In emergency scenarios, immediate reconnaissance efforts are necessary. These efforts often take months to complete in full. While underway, building occupants are unable to return to their homes/businesses, and thus, the impact on the society of the disaster-stricken region is increased. In order to mitigate the impact, researchers have focused on creating a more efficient means of assessing the condition of buildings in the post-disaster state. In this paper, a machine vision-based methodology for real-time post-earthquake safety assessment is presented. A novel method of retrieving spalled properties on reinforced concrete (RC) columns in RC frame buildings using image data is presented. In this method, the spalled region is detected using a local entropy-based approach. Following this, the depth properties are retrieved using contextual information pertaining to the amount and type of reinforcement which is exposed. The method is validated using a dataset of damaged RC column images.This material is based in part upon work supported by the National Science Foundation under Grant Numbers CMMI-1034845 and CMMI-0738417.This is the accepted manuscript. The final version is available from ASCE at http://dx.doi.org/10.1061/(ASCE)ST.1943-541X.000120